




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor





class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def __init__(			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	int = 32  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict=7  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=30  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=400  ,      SCREAMING_SNAKE_CASE__	:	Any=3  ,      ) ->			Tuple:
         lowerCAmelCase__            =					parent
         lowerCAmelCase__            =					do_resize
         lowerCAmelCase__            =					size if size is not None else {"shortest_edge": 288}
         lowerCAmelCase__            =					size_divisor
         lowerCAmelCase__            =					do_rescale
         lowerCAmelCase__            =					rescale_factor
         lowerCAmelCase__            =					do_normalize
         lowerCAmelCase__            =					do_center_crop
         lowerCAmelCase__            =					image_mean
         lowerCAmelCase__            =					image_std
         lowerCAmelCase__            =					do_pad
         lowerCAmelCase__            =					batch_size
         lowerCAmelCase__            =					num_channels
         lowerCAmelCase__            =					min_resolution
         lowerCAmelCase__            =					max_resolution


    def   a (			self	:	str  ) ->			Tuple:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Any=False  ) ->			Optional[Any]:
         if not batched:
              lowerCAmelCase__            =					self.size["shortest_edge"]
              lowerCAmelCase__            =					image_inputs[0]
              if isinstance(SCREAMING_SNAKE_CASE__  ,      Image.Image  ):
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					image.size
              else:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					image.shape[1], image.shape[2]
              lowerCAmelCase__            =					size / min(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
              if h < w:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					size, scale * w
              else:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					scale * h, size

              lowerCAmelCase__            =					int((1_333 / 800) * size  )
              if max(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) > max_size:
                   lowerCAmelCase__            =					max_size / max(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
                   lowerCAmelCase__            =					newh * scale
                   lowerCAmelCase__            =					neww * scale

              lowerCAmelCase__      ,						lowerCAmelCase__            =					int(newh + 0.5  ), int(neww + 0.5  )
              lowerCAmelCase__      ,						lowerCAmelCase__            =					(
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

         else:
              lowerCAmelCase__            =					[]
              for image in image_inputs:
                   lowerCAmelCase__      ,						lowerCAmelCase__            =					self.get_expected_values([image]  )
                   expected_values.append((expected_height, expected_width)  )
              lowerCAmelCase__            =					max(SCREAMING_SNAKE_CASE__  ,      key=lambda SCREAMING_SNAKE_CASE__  : item[0]  )[0]
              lowerCAmelCase__            =					max(SCREAMING_SNAKE_CASE__  ,      key=lambda SCREAMING_SNAKE_CASE__  : item[1]  )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class 					__lowerCamelCase			(       UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = BridgeTowerImageProcessor if is_vision_available() else None


    def   a (			self	:	Tuple  ) ->			int:
         lowerCAmelCase__            =					BridgeTowerImageProcessingTester(self  )


    @property
    def   a (			self	:	str  ) ->			Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()


    def   a (			self	:	List[Any]  ) ->			Dict:
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "image_mean"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "image_std"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "do_normalize"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "do_resize"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "size"  )  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "size_divisor"  )  )


    def   a (			self	:	Any  ) ->			Tuple:
         pass


    def   a (			self	:	Tuple  ) ->			Any:
         # Initialize image processor
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         lowerCAmelCase__            =					prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=SCREAMING_SNAKE_CASE__  )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,      Image.Image  )

         # Test not batched input
         lowerCAmelCase__            =					image_processing(image_inputs[0]  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

         # Test batched
         lowerCAmelCase__            =					image_processing(SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  ,      batched=SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )  ,      )


    def   a (			self	:	Optional[Any]  ) ->			List[Any]:
         # Initialize image processor
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         lowerCAmelCase__            =					prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=SCREAMING_SNAKE_CASE__  ,      numpify=SCREAMING_SNAKE_CASE__  )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,      np.ndarray  )

         # Test not batched input
         lowerCAmelCase__            =					image_processing(image_inputs[0]  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

         # Test batched
         lowerCAmelCase__            =					image_processing(SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  ,      batched=SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )  ,      )


    def   a (			self	:	Optional[Any]  ) ->			Tuple:
         # Initialize image processor
         lowerCAmelCase__            =					self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         lowerCAmelCase__            =					prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=SCREAMING_SNAKE_CASE__  ,      torchify=SCREAMING_SNAKE_CASE__  )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,      torch.Tensor  )

         # Test not batched input
         lowerCAmelCase__            =					image_processing(image_inputs[0]  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

         # Test batched
         lowerCAmelCase__            =					image_processing(SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).pixel_values

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  ,      batched=SCREAMING_SNAKE_CASE__  )
         self.assertEqual(
             encoded_images.shape  ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )  ,      )







'''simple docstring'''






import math

import tensorflow as tf
from packaging import version






def   __A    (				a_    :	List[Any]						):
		lowerCAmelCase  :     Any       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     List[Any]       						= 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)   ,x.dtype						)						))

		return x * cdf






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Dict       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     int       						= tf.cast(math.pi   ,x.dtype						)
		lowerCAmelCase  :     Dict       						= tf.cast(0.0_4_4_7_1_5   ,x.dtype						)
		lowerCAmelCase  :     Dict       						= 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(a_   ,3						))						))

		return x * cdf






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Any       						= tf.convert_to_tensor(a_						)

		return x * tf.tanh(tf.math.softplus(a_						)						)






def   __A    (				a_    :	List[str]						):
		lowerCAmelCase  :     Dict       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     Any       						= tf.cast(0.0_4_4_7_1_5   ,x.dtype						)
		lowerCAmelCase  :     Optional[int]       						= tf.cast(0.7_9_7_8_8_4_5_6_0_8   ,x.dtype						)

		return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     Optional[int]       						= tf.convert_to_tensor(a_						)
		lowerCAmelCase  :     List[Any]       						= tf.cast(1.7_0_2   ,x.dtype						)
		return x * tf.math.sigmoid(coeff * x						)






def   __A    (				a_    :	List[Any]						):
		return tf.clip_by_value(_gelu(a_						)   ,-1_0   ,1_0						)






def   __A    (				a_    :	List[Any]   ,a_    :	List[Any]=-1						):
		lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= tf.split(a_   ,2   ,axis=a_						)
		return a * tf.math.sigmoid(a_						)


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):






					def   __A    (				a_    :	Optional[Any]						):
							return tf.keras.activations.gelu(a_   ,approximate=a_						)

					lowerCAmelCase									=   tf.keras.activations.gelu

					lowerCAmelCase									=   approximate_gelu_wrap
else:
					lowerCAmelCase									=   _gelu
					lowerCAmelCase									=   _gelu_new


lowerCAmelCase									=   {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}






def   __A    (				a_    :	int						):
		if activation_string in ACTaFN:
				return ACTaFN[activation_string]
		else:
				raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}'''						)



